ARTIFICIAL intelligence (AI) is barging its way into business. As our special report this week explains, firms of all types are harnessing AI to forecast demand, hire workers and deal with customers. In 2017 companies spent around $22bn on AI-related mergers and acquisitions, about 26 times more than in 2015….

Introducing TensorFlow.js: Machine Learning in JavascriptPosted by Josh Gordon and Sara Robinson, Developer Advocates – Were excited to introduce TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API.

If youre watching the livestream for the TensorFlow Developer Summit, during the TensorFlow.js talk youll find a demo where @dsmilkov and @nsthorat train a model to control a PAC-MAN game using computer vision and a webcam, entirely in the browser.

If you have an existing TensorFlow or Keras model youve previously trained offline, you can convert into TensorFlow.js format, and load it into the browser for inference.You can re-train an imported model.

You can also use TensorFlow.js to define, train, and run models entirely in the browser using Javascript and a high-level layers API.

TensorFlow.js also includes a Layers API, which is a higher level library for building machine learning models that uses Core, as well as tools for automatically porting TensorFlow SavedModels and Keras hdf5 models.

A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning.

This post is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts.

They go from introductory Python material to deep learning with TensorFlow and Theano, and hit a lot of stops in between.

So here they are: 10 useful IPython Notebook Github repositories in no particular order: – – This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset.

This is an eclectic mix, put together by John Wittenauer, with notebooks for Python implementation of Ng’s Coursera course exercises, Udacity’s TensorFlow-oriented deep learning course exercises, and the Spark edX course exercises.